Correlation Analysis Between IPC and NQTH

This module allows you to analyze existing cross correlation between IPC and NQTH. You can compare the effects of market volatilities on IPC and NQTH and check how they will diversify away market risk if combined in the same portfolio for a given time horizon. You can also utilize pair trading strategies of matching a long position in IPC with a short position of NQTH. See also your portfolio center. Please also check ongoing floating volatility patterns of IPC and NQTH.
Horizon     30 Days    Login   to change

IPC  vs.  NQTH

 Performance (%) 

Pair Volatility

Given the investment horizon of 30 days, IPC is expected to under-perform the NQTH. In addition to that, IPC is 1.82 times more volatile than NQTH. It trades about -0.18 of its total potential returns per unit of risk. NQTH is currently generating about -0.07 per unit of volatility. If you would invest  112,940  in NQTH on November 12, 2018 and sell it today you would lose (3,617)  from holding NQTH or give up 3.2% of portfolio value over 30 days.

Pair Corralation between IPC and NQTH

Time Period2 Months [change]
ValuesDaily Returns


IPC diversification synergy

Very weak diversification

Overlapping area represents the amount of risk that can be diversified away by holding IPC and NQTH in the same portfolio assuming nothing else is changed. The correlation between historical prices or returns on NQTH and IPC is a relative statistical measure of the degree to which these equity instruments tend to move together. The correlation coefficient measures the extent to which returns on IPC are associated (or correlated) with NQTH. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of NQTH has no effect on the direction of IPC i.e. IPC and NQTH go up and down completely randomly.

Comparative Volatility

 Predicted Return Density 

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See also your portfolio center. Please also try Portfolio Backtesting module to avoid under-diversification and over-optimization by backtesting your portfolios.